Papers by Zhe Wang

Enhancing Reinforcement Learning for Radiology Report Generation with Evidence-aware Rewards and Self-correcting Preference Learning (2026.acl-long)

Challenge:	Existing benchmarks focus more on end-to-end performance, but neglect the underlying principles of knowledge acquisition and generalization.
Approach:	They propose a benchmark specifically designed to explore the problem-solving principles by decomposing 6.5K visual math problems into 10.9K step-level questions for evaluation.
Outcome:	The proposed benchmark covers 6.5K visual math problems and 10.9K step-level questions spanning 5 layers of knowledge granularity and 67 hierarchical knowledge concepts.

Copied to clipboard

Qin Zhou, Guoyan Liang, Qianyi Yang, Jingyuan Chen, Sai Wu, Chang Yao, Zhe Wang

CHIFRAUD: A Long-term Web Text Dataset for Chinese Fraud Detection (2025.coling-main)

Challenge:	Recent reinforcement learning approaches have advanced radiology report generation (RRG) however, there are two limitations: report-level rewards offer limited evidence-grounded guidance for clinical faithfulness .
Approach:	They propose a method that uses group-wise evidence-aware alignment rewards and self-correcting preference learning to build a reliable, disease-agnostic preference dataset without human supervision.
Outcome:	ESC-RL promotes clinically faithful, disease-aligned reward and supports continual self-improvement during training.

Copied to clipboard

Min Tang, Lixin Zou, Zhe Jin, ShuJie Cui, Shiuan Ni Liang, Weiqing Wang

Adversarial Domain Adaptation for Machine Reading Comprehension (D19-1)

Challenge:	Detecting fraudulent online text is essential as they exploit human greed and deceive individuals.
Approach:	They propose to build a long-term dataset of Chinese fraudulent texts collected over 12 months.
Outcome:	The proposed dataset includes 59,106 entries extracted from billions of web pages and includes large language model-based detectors and pre-trained language model approaches.

Copied to clipboard

Huazheng Wang, Zhe Gan, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, Hongning Wang

EarlyBERT: Efficient BERT Training via Early-bird Lottery Tickets (2021.acl-long)

Challenge:	Existing models for machine reading comprehension rely on large amounts of human-annotated in-domain data.
Approach:	They propose an unsupervised domain adaptation framework for Machine Reading Comprehension where the source domain has a large amount of labeled data, while only unlabeled passages are available in the target domain.
Outcome:	The proposed framework can be generalizable to different MRC models and datasets and can be extended to semi-supervised learning.

Copied to clipboard

Xiaohan Chen, Yu Cheng, Shuohang Wang, Zhe Gan, Zhangyang Wang, Jingjing Liu

MC2: A Minimum-Coverage and Dataset-Agnostic Framework for Compositional Generalization of LLMs on Semantic Parsing (2025.findings-emnlp)

Challenge:	Large-scale pre-trained language models require enormous computational resources and long training time.
Approach:	They propose an algorithm to reduce inference time and train large NLP models by slimming the self-attention and fully-connected sub-layers inside a transformer.
Outcome:	The proposed algorithm achieves comparable performance to standard BERT with 35 45% less training time.

Copied to clipboard

Ziyao Xu, Zhe Yang, Houfeng Wang

RepoDistill: Distilling Repository Knowledge through Compression-Aware Budget Allocation and Policy Optimization (2026.findings-acl)

Challenge:	Existing research relies on dataset-specific designs or a large number of samples to improve compositional generalization of large language models (LLMs) .
Approach:	They propose a minimum-coverage framework that can help LLMs achieve compositional generalization by selecting and organizing samples that satisfy the primitive coverage.
Outcome:	The proposed framework can improve compositional generalization on different parsing datasets in the minimum-coverage setting.

Copied to clipboard

Xin Yin, Zixiang Ding, Yiang Zhang, Qiang Wang, Rui Wang, Chao Ni, Zhe Cui

SG-FSM: A Self-Guiding Zero-Shot Prompting Paradigm for Multi-Hop Question Answering Based on Finite State Machine (2025.findings-naacl)

Challenge:	Large Language Models (LLMs) have strong performance on code translation tasks, but they struggle with repository-level scenarios where context is extensive and interdependent.
Approach:	They propose a framework that integrates retrieval with learning budget allocation for fine-grained context compression.
Outcome:	The proposed framework outperforms baselines on SWE-QA, CoderEval, and LongCodeU.

Copied to clipboard

Xiaochen Wang, Junqing He, Liang Chen, Gholamreza Haffari, Yiru Wang, Zhe Yang, Xiangdi Meng, Kunhao Pan, Zhifang Sui

SLARD: A Chinese Superior Legal Article Retrieval Dataset (2025.coling-main)

Challenge:	Multi-hop Question Answering (MHQA) is a challenging task that requires models to answer multiple questions with multiple passages.
Approach:	They propose a self-guided prompting finite state machine to improve multi-hop reasoning abilities by iterating over multiple questions and correcting itself to improve accuracy.
Outcome:	The proposed approach outperforms baselines on Musique and other datasets.

Copied to clipboard

Zhe Chen, Pengjie Ren, Fuhui Sun, Xiaoyan Wang, Yujun Li, Siwen Zhao, Tengyi Yang

Information Retrieval

Challenge:	Existing retrieval methods struggle to achieve ideal results, a study finds . existing large language models lack prior knowledge of the content of superior legal articles .
Approach:	They propose to use a Chinese superior legal article retrieval dataset to find relevant articles with higher legal effectiveness.
Outcome:	The proposed dataset shows that existing retrieval methods struggle to achieve ideal results.

E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition (2023.findings-acl)

Copied to clipboard

Zhen Zhang, Mengting Hu, Shiwan Zhao, Minlie Huang, Haotian Wang, Lemao Liu, Zhirui Zhang, Zhe Liu, Bingzhe Wu

OptiCo: Adaptive Distributed Training Optimization via Collaborative Agent Reasoning (2026.acl-long)

Challenge:	Named entity recognition (NER) systems focus on improving model performance, ignoring the need to quantify model uncertainty.
Approach:	They propose to introduce two uncertainty-guided loss terms to the conventional EDL and a series of uncertainty-guiding training strategies to solve these challenges.
Outcome:	The proposed method achieves better OOV/OOD detection performance and generalization ability on OOV entities compared to state-of-the-art methods.

Copied to clipboard

Sheng Chen, Tang Zhe, Weixing Zhang, Fei Yang, Yuanyuan. Wang, Tianlin Li, Yang Liu

TurboFuzzLLM: Turbocharging Mutation-based Fuzzing for Effectively Jailbreaking Large Language Models in Practice (2025.naacl-industry)

Challenge:	Existing distributed training frameworks are plagued by over-reliance on prior profiling and poor generalization across models/hardware.
Approach:	They propose a model-driven multi-agent framework that leverages Large Language Models to enable automatic and explainable distributed training strategy configuration.
Outcome:	The proposed framework outperforms expert-designed training strategies within 20 iterations.

Copied to clipboard

Aman Goel, Xian Wu, Zhe Wang, Dmitriy Bespalov, Yanjun Qi

Cluster-Former: Clustering-based Sparse Transformer for Question Answering (2021.findings-acl)

Challenge:	Existing methods for jailbreaking large-language models are limited by their limitations . authors present a mutation-based fuzzing technique that generates effective jailbreaking templates .
Approach:	They propose a mutation-based fuzzing technique for efficiently finding effective jailbreaking templates that combine with harmful questions to generate harmful responses.
Outcome:	The proposed technique achieves 95% attack success rates on public datasets for leading LLMs . it also shows impressive generalizability to unseen harmful questions and improves model defenses to prompt attacks.

Copied to clipboard

Shuohang Wang, Luowei Zhou, Zhe Gan, Yen-Chun Chen, Yuwei Fang, Siqi Sun, Yu Cheng, Jingjing Liu

Text Clustering

Challenge:	Existing models for encoding long sequences in deep learning suffer from high latency and memory demands.
Approach:	They propose a clustering-based sparse Transformer framework to perform attention across chunked sequences.
Outcome:	The proposed framework achieves state-of-the-art on several major QA benchmarks.

Fine-grained Video Dubbing Duration Alignment with Segment Supervised Preference Optimization (2025.acl-long)

Copied to clipboard

Chaoqun Cui, Liangbin Huang, Shijing Wang, Zhe Tong, Zhaolong Huang, Xiao Zeng, Xiaofeng Liu

Are AI-Generated Text Detectors Robust to Adversarial Perturbations? (2024.acl-long)

Challenge:	Video dubbing systems use neural machine translation and text-to-speech technologies to translate original speech into visual media programs.
Approach:	They propose a preference optimization method to optimize video dubbing duration alignment . they propose combining segment-wise sampling and fine-grained loss to mitigate duration mismatches .
Outcome:	The proposed method achieves superior performance in duration alignment tasks.

Copied to clipboard

Guanhua Huang, Yuchen Zhang, Zhe Li, Yongjian You, Mingze Wang, Zhouwang Yang

Robustness in NLP

Challenge:	Existing detectors for AI-generated text lack robustness against adversarial perturbations, with even minor changes in characters or words causing a reversal in distinguishing between human-created and AI-generated text.
Approach:	They propose a siamese calibration technique to train the model to make equally confident predictions under different noise, which improves the model’s robustness against adversarial perturbations.
Outcome:	The proposed detector outperforms baseline methods on four datasets and is more generalizable in cross-domain, cross-genre, and mixed-source scenarios.

Multi-Step Generation of Test Specifications using Large Language Models for System-Level Requirements (2025.acl-industry)

Copied to clipboard

Dragan Milchevski, Gordon Frank, Anna Hätty, Bingqing Wang, Xiaowei Zhou, Zhe Feng

MMedAgent: Learning to Use Medical Tools with Multi-modal Agent (2024.findings-emnlp)

Challenge:	System-level testing is a critical phase in the development of large, safety-dependent systems, such as those in the automotive industry.
Approach:	They propose an AI-powered assistant to aid users in creating test specifications for system-level requirements.
Outcome:	The proposed system reduces the effort required to derive test specifications by 30% in ROUGE-L.

Copied to clipboard

Binxu Li, Tiankai Yan, Yuanting Pan, Jie Luo, Ruiyang Ji, Jiayuan Ding, Zhe Xu, Shilong Liu, Haoyu Dong, Zihao Lin, Yixin Wang

SQL-ASTRA: Alleviating Sparse Feedback in Agentic SQL via Column-Set Matching and Trajectory Aggregation (2026.findings-acl)

Challenge:	Multi-modal Large Language Models (MLLMs) exhibit limited generality and often fall short when compared to specialized models.
Approach:	They propose a multi-modal medical agent that picks the most suitable medical tools based on user inputs.
Outcome:	The proposed agent performs better than open-source models and the closed-source model, GPT-4o.

Copied to clipboard

Long Li, Zhijian Zhou, Jiangxuan Long, Peiyang Liu, Weidi Xu, Zhe Wang, Shirui Pan, Chao Qu

Programming Languages in NLP

Challenge:	Agentic SQL is a framework for multiturn agent learning, but it is limited to single-turn paradigms.
Approach:	They propose a framework that provides a universal two-tiered reward mechanism for credit assignment . they propose 'Aggregated Trajectory Reward' to resolve multi-turn credit assignment.
Outcome:	The proposed framework outperforms SOTA Arctic-Text2SQL-R1-7B on BIRD and Spider 2.0 using identical models.

A New Approach to Overgenerating and Scoring Abstractive Summaries (2021.naacl-main)

Copied to clipboard

Kaiqiang Song, Bingqing Wang, Zhe Feng, Fei Liu

Contrastive Distillation on Intermediate Representations for Language Model Compression (2020.emnlp-main)

Challenge:	Abstractive summarization is a learning objective to produce system outputs that resemble reference summaries on a word-to-word basis.
Approach:	They propose a two-staged strategy to generate multiple variants of the target summary and score and select admissible ones according to users’ needs.
Outcome:	The proposed approach can achieve state-of-the-art on benchmark summarization datasets.

Copied to clipboard

Siqi Sun, Zhe Gan, Yuwei Fang, Yu Cheng, Shuohang Wang, Jingjing Liu

Topic-Guided Variational Auto-Encoder for Text Generation (N19-1)

Challenge:	Existing methods to compress language models use a simple L_2 loss to distill knowledge in the intermediate representations of a large BERT model to a smaller one.
Approach:	They propose a method that uses knowledge distillation to distill knowledge through intermediate layers of the teacher via a contrastive objective.
Outcome:	The proposed method outperforms state-of-the-art methods on the GLUE benchmark.

Copied to clipboard

Wenlin Wang, Zhe Gan, Hongteng Xu, Ruiyi Zhang, Guoyin Wang, Dinghan Shen, Changyou Chen, Lawrence Carin

DUAL RM: Beyond Rule-based Preference Reward Modeling via Meta-Reward (2026.acl-long)

Challenge:	Experimental results show that our model outperforms its competitors on both unconditional and conditional text generation.
Approach:	They propose a topic-guided variational auto-encoder model for text generation that specifies a Gaussian mixture model and a neural topic module to generate sentences under the topic.
Outcome:	The proposed model outperforms existing variational auto-encoders on unconditional and conditional text generation, and can generate semantically-meaningful sentences with various topics.

Copied to clipboard

Xiaobo Liang, Wanfu Wang, Qipeng Huang, Yuyang Ding, Zecheng Tang, Yixin Ji, Qianben Chen, Zhe Zhao, Kehai Chen, Juntao Li, Min Zhang

Challenge:	Existing preference-based reward modeling methods face a recursive dependency where each verifier requires a meta-verifier, leading to continuous and costly dependence on human annotation.
Approach:	They propose a dual RM that couples discriminative and generative reward models under a non-parametric meta-reward.
Outcome:	The proposed model achieves strong performance across major preference benchmarks and even when trained exclusively on language modality, it exhibits robust cross-modal transfer on Omni-RewardBench.

ToolDreamer: Instilling LLM Reasoning Into Tool Retrievers (2026.eacl-long)

Copied to clipboard

Saptarshi Sengupta, Zhengyu Zhou, Jun Araki, Xingbo Wang, Bingqing Wang, Suhang Wang, Zhe Feng

DICE: Structured Reasoning in LLMs through SLM-Guided Chain-of-Thought Correction (2025.emnlp-main)

Challenge:	Existing retrieval models rank tools based on similarity between query and tool description (TD) Existing tools are not conditioned to learn tool-to-tool relationships (middle).
Approach:	They propose a framework that conditions retrieval models to fetch tools based on hypothetical (synthetic) TD generated using an LLM.
Outcome:	The proposed framework improves the performance of sparse and dense retrievers with and without training, showcasing its flexibility.

Copied to clipboard

Yiqi Li, Yusheng Liao, Zhe Chen, Yanfeng Wang, Yu Wang

Confidence v.s. Critique: A Decomposition of Self-Correction Capability for LLMs (2025.acl-long)

Challenge:	Large language models (LLMs) often prioritize reasoning over adherence to detailed instructions due to high computational costs and limited parameter access.
Approach:	They propose a lightweight framework that guides small language models to refine LLMs’ outputs through chain-of-thought correction.
Outcome:	The proposed framework improves the average format accuracy and content correctness of LLM outputs by 35.4% and 29.4%, respectively, achieving state-of-the-art (SOTA) performance over other competitive baselines.

Copied to clipboard

Zhe Yang, Yichang Zhang, Yudong Wang, Ziyao Xu, Junyang Lin, Zhifang Sui

Challenge:	Existing approaches to improve self-correction performance of Large Language Models are based on intrinsic selfcorrectione, which allows the model to check and revise its selfgenerated answers without external feedback.
Approach:	They propose to decompose the self-correction capability into confidence and critique capabilities and a metric for overall self-corretion capability evaluation.
Outcome:	The proposed method outperforms vanilla SFT and achieves much higher accuracy after self-correction.

An Empirical Study of Multimodal Model Merging (2023.findings-emnlp)

Copied to clipboard

Yi-Lin Sung, Linjie Li, Kevin Lin, Zhe Gan, Mohit Bansal, Lijuan Wang

DelucionQA: Detecting Hallucinations in Domain-specific Question Answering (2023.findings-emnlp)

Challenge:	Existing studies have shown that model merging can generate a multi-task solution without synchronous training.
Approach:	They propose to merge vision, language, and cross-modal transformers of a modality-specific architecture to create a parameter-efficient architecture.
Outcome:	The proposed model merging outperforms naive models on various tasks with improvements of 3% on VQA, 7% on COCO retrieval, 25% on NLVR2, 14% on Flickr30k and 3% ADE20k.

Copied to clipboard

Mobashir Sadat, Zhengyu Zhou, Lukas Lange, Jun Araki, Arsalan Gundroo, Bingqing Wang, Rakesh Menon, Md Parvez, Zhe Feng

AskToAct: Enhancing LLMs Tool Use via Self-Correcting Clarification (2025.emnlp-main)

Challenge:	Hallucination is a well-known phenomenon in text generated by large language models . state-of-the-art LLMs still have a number of weaknesses, including the tendency to generate hallucinatory statements without considering the factuality .
Approach:	They propose a dataset that captures hallucinations made by retrieval-augmented LLMs . they propose to use these methods to help detect hallucinosity in QA tasks .
Outcome:	The proposed method captures hallucinations made by retrieval-augmented LLMs for QA tasks.

Copied to clipboard

Xuan Zhang, Yongliang Shen, Zhe Zheng, Linjuan Wu, Wenqi Zhang, Yuchen Yan, Qiuying Peng, Jun Wang, Weiming Lu

TutorialVQA: Question Answering Dataset for Tutorial Videos (2020.lrec-1)

Challenge:	Existing tools for ambiguous and incomplete queries are limited by manual construction and lack of error correction mechanisms during multi-turn clarification.
Approach:	They propose a framework that exploits the mapping between queries and their tool invocation solutions by removing key parameters from queries while retaining them as ground truth.
Outcome:	The proposed framework outperforms existing methods while maintaining high accuracy in tool invocation.

Copied to clipboard

Anthony Colas, Seokhwan Kim, Franck Dernoncourt, Siddhesh Gupte, Zhe Wang, Doo Soon Kim

Visual Data in NLP

Challenge:	a new question answering task on instructional videos is needed due to their verbose nature . factoid questions are only a small part of what people actually want to ask on video contents .
Approach:	They propose a question answering task on instructional videos based on video transcripts . they use a dataset consisting of 6,000 manually collected triples of (video, question, answer span)
Outcome:	The proposed task focuses on screencast tutorial videos pertaining to an image editing program.

DSVD: Dynamic Self-Verify Decoding for Faithful Generation in Large Language Models (2025.emnlp-main)

Copied to clipboard

YiQiu Guo, Yuchen Yang, Zhe Chen, Pingjie Wang, Yusheng Liao, Ya Zhang, Yanfeng Wang, Yu Wang

Theory-optimal Quantization Based on Flatness (2026.acl-long)

Challenge:	Existing approaches to reliability of large language models often lack self-correction or use costly post-hoc verification.
Approach:	They propose a decoding framework that enhances generation reliability through real-time hallucination detection and efficient error correction.
Outcome:	Extensive experiments across five benchmarks show the proposed framework improves truthfulness and factual accuracy.

Copied to clipboard

Xiusheng Huang, Zhe Li, Xuanwu Yin, Lu Wang, Yequan Wang, Dong Li, Emad Barsoum, Kang Liu

Linguistic Theories

Challenge:	Recent approaches to quantization of Large Language Models (LLMs) have been widely adopted due to activation outliers, which degrade model performance especially at lower bit precision.
Approach:	They propose a new metric for quantization that strategically distributes outlier magnitudes across matrix dimensions via optimized diagonal operations.
Outcome:	The proposed framework achieves less than 1% accuracy drop in W4A4 quantization on the LLaMA-3-8B model and reduces the performance gap by 39.1% on the more challenging W2A4KV16 model.

CLUE: A Chinese Language Understanding Evaluation Benchmark (2020.coling-main)

Copied to clipboard

Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, Zhenzhong Lan

Challenge:	Existing language evaluation benchmarks for English are limited to English . lack of such benchmarks makes it difficult to replicate success in other languages .
Approach:	They introduce a large-scale Chinese language understanding evaluation benchmark . the benchmark uses a set of current state-of-the-art pre-trained Chinese models .
Outcome:	The first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark is released . the benchmark evaluates models across a wide range of tasks on original Chinese text . existing language evaluation benchmarks are mostly limited to English .

Specialization without Sparsity: Efficient and Expressive Split-Path Experts for LLM Fine-Tuning (2026.findings-acl)

Copied to clipboard

Ganghao Liu, Qin Zhou, Zhe Wang, Xuehan Lu, Haihua Huang, Yunfei Tong, Heng Tian

Glyph: Scaling Context Windows via Visual-Text Compression (2026.acl-long)

Challenge:	Parameter-efficient fine-tuning (PEFT) is a low-cost alternative to full fine-timing due to the massive overhead.
Approach:	They propose a Mixture-of-Experts approach that enhances specialization while maintaining low resource overhead.
Outcome:	The proposed approach outperforms or matches state-of-the-art methods on GLUE, GSM8K, MBPP, and a text rewriting task from SmolTalk.

Copied to clipboard

Jiale Cheng, Yusen Liu, Xinyu Zhang, Yulin Fei, Wenyi Hong, Ruiliang Lyu, Weihan Wang, Zhe Su, Xiaotao Gu, Xiao Liu, Yushi Bai, Jie Tang, Hongning Wang, Minlie Huang

Towards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large Language Models in Medical Applications (2025.acl-long)

Challenge:	Large language models (LLMs) traditionally represent text as sequences of discrete tokens . a long-context scaling problem requires processing more tokens more efficiently .
Approach:	They propose a framework that renders long texts into compact visual pages and processes them with a vision-language model.
Outcome:	The proposed framework renders long texts into compact visual pages and processes them with a vision-language model.

Copied to clipboard

Zhe Chen, Yusheng Liao, Shuyang Jiang, Pingjie Wang, YiQiu Guo, Yanfeng Wang, Yu Wang

AGENTVIGIL: Automatic Black-Box Red-teaming for Indirect Prompt Injection against LLM Agents (2025.findings-emnlp)

Challenge:	Existing approaches to source planning fail to achieve this due to misalignment between the model’s expectation of the sources and their actual content.
Approach:	They propose a method to optimise large-scale medical knowledge models by combining multiple medical knowledge sources into one query.
Outcome:	The proposed method significantly improves multi-source planning performance while training a smaller model to learn source alignment.

Copied to clipboard

Zhun Wang, Vincent Siu, Zhe Ye, Tianneng Shi, Yuzhou Nie, Xuandong Zhao, Chenguang Wang, Wenbo Guo, Dawn Song

Hyperbolic Hierarchy-Aware Knowledge Graph Embedding for Link Prediction (2021.findings-emnlp)

Challenge:	AGENTVIGIL is a black-box optimization framework to exploit indirect prompt injection vulnerabilities . indirect prompts compromise the core of LLM agents by manipulating contextual information rather than direct user prompts.
Approach:	They propose a black-box optimization framework to exploit indirect prompt injection vulnerabilities . they use a Monte Carlo tree-based algorithm to iteratively refine inputs .
Outcome:	The proposed framework achieves 71% and 70% success rates against two public benchmarks .

Copied to clipboard

Zhe Pan, Peng Wang

CuMA: Aligning LLMs with Sparse Cultural Values via Demographic-Aware Mixture of Adapters (2026.acl-long)

Challenge:	Existing knowledge graph embedding methods are built on Euclidean space, which are difficult to handle hierarchical structures.
Approach:	They propose a KGE model with extended Poincaré Ball and polar coordinate system to capture hierarchical structures.
Outcome:	The proposed model captures hierarchical relationships with extended Poincaré Ball and polar coordinate system in hyperbolic space and achieves state-of-the-art results on part of link prediction tasks.

Copied to clipboard

Ao Sun, Xiaoyu Wang, Zhe Tan, Yu Li, Zhu Jiachen, Yuheng Jia, Shu Su

MARE: Multi-Aspect Rationale Extractor on Unsupervised Rationale Extraction (2024.emnlp-main)

Challenge:	Large Language Models (LLMs) have a global audience, so alignment must extend to cultural resonance.
Approach:	They propose a framework that frames alignment as a conditional capacity separation problem.
Outcome:	The proposed framework outperforms both dense baselines and semantic-only MoEs on three large language models.

Copied to clipboard

Han Jiang, Junwen Duan, Zhe Qu, Jianxin Wang

Does Visual Grounding Enhance the Understanding of Embodied Knowledge in Large Language Models? (2025.findings-emnlp)

Challenge:	Existing methods to extract text snippets from input text to support model predictions without explicit rationale annotation have limited their ability to capture meaningful internal correlations between aspects.
Approach:	They propose a multi-aspect rationale extractor that extracts text snippets to support model predictions without explicit rationale annotation.
Outcome:	The proposed method achieves state-of-the-art on two unsupervised rationale extraction benchmarks.

Copied to clipboard

Zhihui Yang, Yupei Wang, Kaijie Mo, Zhe Zhao, Renfen Hu

MedCare: Advancing Medical LLMs through Decoupling Clinical Alignment and Knowledge Aggregation (2024.findings-emnlp)

Challenge:	Despite significant progress in multimodal language models, it remains unclear whether visual grounding enhances their understanding of embodied knowledge compared to text-only models.
Approach:	They propose to assess vision-language models’ perceptual abilities across different sensory modalities through vector comparison and question-answering tasks with over 1,700 questions.
Outcome:	The proposed benchmark assesses the models’ perceptual abilities across different sensory modalities through vector comparison and question-answering tasks with over 1,700 questions.

Copied to clipboard

Yusheng Liao, Shuyang Jiang, Zhe Chen, Yu Wang, Yanfeng Wang

Challenge:	Large language models (LLMs) have made significant progress in natural language understanding and generation, proving valuable especially in the medical field.
Approach:	They propose a medical LLM through decoupling Clinical Alignment and Knowledge Aggregation which uses a and a to encode diverse knowledge in the first stage and filter out detrimental information.
Outcome:	The proposed model achieves promising performance on over 20 medical tasks and specific medical alignment tasks.

Bayesian Calibration of Win Rate Estimation with LLM Evaluators (2024.emnlp-main)

Copied to clipboard

Yicheng Gao, Gonghan Xu, Zhe Wang, Arman Cohan

Cross-Thought for Sentence Encoder Pre-training (2020.emnlp-main)

Challenge:	Recent advances in large language models (LLMs) show the potential of using LLMs as evaluators for text quality evaluation.
Approach:	They propose two methods to improve the accuracy of LLM evaluators by Bayesian inference.
Outcome:	The proposed methods improve the accuracy of the win rate estimation using LLMs . the proposed methods are based on six datasets covering story generation, summarization, and instruction following tasks .

Copied to clipboard

Shuohang Wang, Yuwei Fang, Siqi Sun, Zhe Gan, Yu Cheng, Jingjing Liu, Jing Jiang

How to Make LMs Strong Node Classifiers? (2026.findings-eacl)

Challenge:	Existing models to pretrain sentence encoders with large unlabeled corpus are lacking in linguistic information retrieval.
Approach:	They propose a novel approach to pre-training sequence encoder using transformers . they propose to train a Transformer-based sequence encoded over a large set of short sequences based on a set of masked words .
Outcome:	The proposed approach outperforms state-of-the-art encoders on hotpotQA by improving intermediate information retrieval performance.

Copied to clipboard

Zhe Xu, Kaveh Hassani, Si Zhang, Hanqing Zeng, Michihiro Yasunaga, Limei Wang, Dongqi Fu, Ning Yao, Bo Long, Hanghang Tong

Information Retrieval

Challenge:	Language Models (LMs) are increasingly challenging the dominance of domain-specific models, such as Graph Neural Networks (GNNs) and Graph Transformers (GTs).
Approach:	They propose a novel approach that empowers off-the-shelf LMs to achieve performance comparable to state-of-the art (SOTA) GNNs on node classification tasks without requiring any architectural modifications.
Outcome:	The proposed approach outperforms existing GNNs on node classification tasks and is open-source upon publication.

Guided by the Plan: Enhancing Faithful Autoregressive Text-to-Audio Generation with Guided Decoding (2026.eacl-long)

Copied to clipboard

Juncheng Wang, Zhe Hu, Chao Xu, Siyue Ren, Yuxiang Feng, Yang Liu, Baigui Sun, Shujun Wang

Cross-Modal Coreference Alignment: Enabling Reliable Information Transfer in Omni-LLMs (2026.acl-long)

Challenge:	Autoregressive (AR) models excel at generating temporally coherent audio by producing tokens sequentially, yet they often falter in faithfully following complex textual prompts.
Approach:	They propose a lightweight auxiliary model trained with a GAE-inspired objective to predict final instruction-following quality from partial generations.
Outcome:	The proposed model achieves 10 points improvement in CLAP score over baseline AR models while maintaining computational parity with best-of-N decoding.

Copied to clipboard

Hongcheng Liu, Yuhao Wang, Zhe Chen, Pingjie Wang, Zhiyuan Zhu, Yixuan Hou, Yanfeng Wang, Yu Wang

M3AV: A Multimodal, Multigenre, and Multipurpose Audio-Visual Academic Lecture Dataset (2024.acl-long)

Challenge:	Experiments on 13 Omni-LLMs reveal systematic weaknesses in cross-modal coreference . cross-module coreference is a crucial missing piece for advancing robust omni-modal reasoning.
Approach:	They propose a cross-modal coreference problem to evaluate and enhance Omni-LLMs' reasoning capabilities.
Outcome:	Experiments on 13 Omni-LLMs show they lack coreference-aware thinking patterns . the CROSSOMNI dataset yields significant performance gains and generalizes well to collaborative reasoning tasks.

Copied to clipboard

Zhe Chen, Heyang Liu, Wenyi Yu, Guangzhi Sun, Hongcheng Liu, Ji Wu, Chao Zhang, Yu Wang, Yanfeng Wang

MultiAgentBench : Evaluating the Collaboration and Competition of LLM agents (2025.acl-long)

Challenge:	Publishing open-source academic video recordings is an emerging approach to sharing knowledge online.
Approach:	They propose a multimodal, multigenre, and multipurpose audio-visual academic lecture dataset with human annotations for multimodal content recognition and understanding tasks.
Outcome:	The proposed dataset can be used for multiple audio-visual recognition and understanding tasks.

Copied to clipboard

Kunlun Zhu, Hongyi Du, Zhaochen Hong, Xiaocheng Yang, Shuyi Guo, Zhe Wang, Zhenhailong Wang, Cheng Qian, Robert Tang, Heng Ji, Jiaxuan You

Challenge:	Large Language Models (LLMs) have shown remarkable capabilities as autonomous agents, yet existing benchmarks focus on single-agent tasks or are confined to narrow domains, failing to capture the dynamics of multi-agent coordination and competition.
Approach:	They propose a benchmark to evaluate LLM-based multi-agent systems across diverse, interactive scenarios.
Outcome:	The proposed framework measures task completion and quality of collaboration and competition using novel, milestone-based key performance indicators.

Mixture-of-Subspaces in Low-Rank Adaptation (2024.emnlp-main)

Copied to clipboard

Taiqiang Wu, Jiahao Wang, Zhe Zhao, Ngai Wong

M3: A Multi-Task Mixed-Objective Learning Framework for Open-Domain Multi-Hop Dense Sentence Retrieval (2024.lrec-main)

Challenge:	Using a subspace-inspired Low-Rank Adaptation method, large language models can be optimized for downstream tasks using parameter-efficient finetuning.
Approach:	They propose a subspace-inspired Low-Rank Adaptation method that decomposes LoRA weights into two subspaces and merges them into the frozen original weight.
Outcome:	The proposed method outperforms LoRA on commonsense reasoning, visual instruction tuning, and subject-driven text-to-image generation tasks.

Copied to clipboard

Yang Bai, Anthony Colas, Christan Grant, Zhe Wang

Enhancing Learning-Based Binary Code Similarity Detection Model through Adversarial Training with Multiple Function Variants (2024.findings-emnlp)

Challenge:	Recent research shows that contrastive learning can lead to suboptimal retrieval performance.
Approach:	They propose an advanced recursive Multi-hop dense sentence retrieval system built upon a novel Multi-task Mixed-objective approach for dense text representation learning.
Outcome:	The proposed approach yields state-of-the-art performance on a large-scale open-domain fact verification benchmark dataset, FEVER.

Copied to clipboard

Lichen Jia, Chenggang Wu, Bowen Tang, Peihua Zhang, Zihan Jiang, Yang Yang, Ning Liu, Jingfeng Zhang, Zhe Wang

EvolveBench: A Comprehensive Benchmark for Assessing Temporal Awareness in LLMs on Evolving Knowledge (2025.acl-long)

Challenge:	Existing Learning-Based Binary Code Similarity Detection (LB-BCSD) methods exhibit lower accuracy in recognizing functions with the same functionality but different implementations.
Approach:	They propose a gradient-guided adversarial attack method based on critical code called FuncFooler which perturbs critical code to generate multiple variants of the same function.
Outcome:	The proposed method increases the accuracy of the current Learning-Based Binary Code Similarity Detection (LB-BCSD) model by 5%-7%.

Copied to clipboard

Zhiyuan Zhu, Yusheng Liao, Zhe Chen, Yuhao Wang, Yunfeng Guan, Yanfeng Wang, Yu Wang

Challenge:	Existing studies have explored how LLMs perceive time, but they often overlook the critical aspect of knowledge utilization.
Approach:	They propose a benchmark that evaluates temporal competence along five key dimensions: Cognition, Awareness, Trustworthiness and reasoning.
Outcome:	EvolveBench measures temporal competence along five key dimensions: Cognition, Awareness, Trustworthiness, Understanding and reasoning.

JTAV: Jointly Learning Social Media Content Representation by Fusing Textual, Acoustic, and Visual Features (C18-1)

Copied to clipboard

Hongru Liang, Haozheng Wang, Jun Wang, Shaodi You, Zhe Sun, Jin-Mao Wei, Zhenglu Yang

Representation Learning

Challenge:	Existing studies on learning social media content focus on single modal or bi-modal learning, but this approach is non-trivial and challenging because content is multi-modal and involves several types of data, including text, audio, and image.
Approach:	They propose to combine textual, acoustic, and visual information to learn social media content by fusing them jointly.
Outcome:	The proposed model outperforms the state-of-the-art approaches on real-world datasets by a large margin.

Rethinking Kullback-Leibler Divergence in Knowledge Distillation for Large Language Models (2025.coling-main)

Copied to clipboard

Taiqiang Wu, Chaofan Tao, Jiahao Wang, Runming Yang, Zhe Zhao, Ngai Wong

Language Model Based Text-to-Audio Generation: Anti-Causally Aligned Collaborative Residual Transformers (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) have been used in Knowledge Distillation (KD) to compress large models.
Approach:	They propose a Kullback-Leiber divergence method which adaptively allocates weights to combine RKL and FKL to reduce the size of Large Language Models (LLMs).
Outcome:	The proposed method outperforms baselines and improves diversity and quality of generated responses.

Copied to clipboard

Juncheng Wang, Chao Xu, Cheng Yu, Zhe Hu, Haoyu Xie, Guoqi Yu, Lei Shang, Shujun Wang

Argument Generation with Retrieval, Planning, and Realization (P19-1)

Challenge:	Autoregressive language models excel in text-to-audio generation, but lag behind diffusion models by a non-trivial margin.
Approach:	They propose a framework that integrates multiple isolated transformers with causal conditioning and anti-causal alignment via reinforcement learning.
Outcome:	The proposed framework outperforms existing LM-based and diffusion-based systems in audio synthesis.

Copied to clipboard

Xinyu Hua, Zhe Hu, Lu Wang

RFBFN: A Relation-First Blank Filling Network for Joint Relational Triple Extraction (2022.acl-srw)

Challenge:	a novel argument generation framework is used to generate counter-arguments . CANDELA uses a text planning decoder to retrieve arguments of different perspectives .
Approach:	They propose a powerful retrieval system and a novel two-step argument generation framework . they use a retrieval-based retrieval platform indexed with 12 million articles from Wikipedia .
Outcome:	The proposed framework yields higher BLEU, ROUGE, and METEOR scores than state-of-the-art models.

Copied to clipboard

Zhe Li, Luoyi Fu, Xinbing Wang, Haisong Zhang, Chenghu Zhou

Hierarchical Graph Network for Multi-hop Question Answering (2020.emnlp-main)

Challenge:	Existing methods for relational triple extraction ignore semantic information of relations or predict subjects and objects sequentially.
Approach:	They propose a relation-first blank filling network to capture semantic information of relations . they transform relations into relation templates with blanks which contain the fine-grained semantic representation of relations.
Outcome:	The proposed model outperforms current state-of-the-art methods on public benchmark datasets.

Copied to clipboard

Yuwei Fang, Siqi Sun, Zhe Gan, Rohit Pillai, Shuohang Wang, Jingjing Liu

Structured Data in NLP

Challenge:	Existing multi-hop question answering models focus on multi-level reasoning across multiple documents or paragraphs.
Approach:	They propose a hierarchical graph network that aggregates clues from scattered texts . they use a set of contextual encoders to initialize nodes on different levels of granularity .
Outcome:	The proposed model outperforms existing multi-hop QA approaches on the HotpotQA benchmark.

Meeseeks: A Feedback-Driven, Iterative Self-Correction Benchmark evaluating LLMs’ Instruction Following Capability (2026.findings-acl)

Copied to clipboard

Jiaming Wang, Yunke Zhao, Peng Ding, Jun Kuang, Yibin Shen, Zhe Tang, Yilin Jin, ZongYu Wang, Xiaoyu Li, Xuezhi Cao

Augmented Adversarial Trigger Learning (2025.findings-naacl)

Challenge:	Existing models lack the ability to adhere to instructions, resulting in suboptimal performance.
Approach:	They propose an automated iterative instruction-following benchmark with integrated feedback mechanism.
Outcome:	The proposed benchmark identifies erroneous components in model responses and provides feedback accurately.

Copied to clipboard

Zhe Wang, Yanjun Qi

Seeing the Whole Elephant: A Benchmark for Failure Attribution in LLM-based Multi-Agent Systems (2026.acl-long)

Challenge:	Gradient optimization-based adversarial attack methods can generate jailbreak prompts or leak system prompts.
Approach:	They propose an algorithm that enhances negative log-likelihood loss and augments it with auxiliary loss.
Outcome:	The proposed approach outperforms current state-of-the-art techniques in nearly 100% of attacks while requiring 80% fewer queries.

Copied to clipboard

Mengzhuo Chen, Junjie Wang, Fangwen Mu, Yawen Wang, Zhe Liu, Huanxiang Feng, Qing Wang

POINTER: Constrained Progressive Text Generation via Insertion-based Generative Pre-training (2020.emnlp-main)

Challenge:	Existing benchmarks rely on partially observable traces that capture only agent outputs . lack of full execution traces obscures many failure causes, authors argue .
Approach:	They propose a benchmark that allows attribution under full execution observability . they find full traces improve attribution accuracy by up to 76.5% over a partial-observation counterpart .
Outcome:	The proposed benchmark improves attribution accuracy by up to 76.5% over a partial-observation counterpart.

Copied to clipboard

Yizhe Zhang, Guoyin Wang, Chunyuan Li, Zhe Gan, Chris Brockett, Bill Dolan

SOP-Maze: Evaluating Large Language Models on Complicated Business Standard Operating Procedures (2026.findings-acl)

Challenge:	Existing pre-trained language models cannot be directly employed to generate text under specified lexical constraints.
Approach:	They propose a method for insertion-based text generation that inserts tokens between existing tokens in a parallel manner.
Outcome:	The proposed method is intuitive and interpretable on Wikipedia and Yelp datasets.

Copied to clipboard

Jiaming Wang, Zhe Tang, Zehao Jin, Hefei Chen, Yilin Jin, Peng Ding, Xiaoyu Li, Xuezhi Cao

A Probabilistic Inference Scaling Theory for LLM Self-Correction (2025.emnlp-main)

Challenge:	Large language models (LLMs) are widely deployed as domain-specific agents, but evaluation of their capabilities in such contexts has not been fully explored.
Approach:	They propose a benchmark to evaluate LLMs' ability to follow instructions and make decisions in real-world scenarios.
Outcome:	The proposed benchmark is constructed from real-world business data and adapted into 23 complex SOP scenarios.

Copied to clipboard

Zhe Yang, Yichang Zhang, Yudong Wang, Ziyao Xu, Junyang Lin, Zhifang Sui

Linguistic Theories

Challenge:	Large Language Models (LLMs) have demonstrated the capability to refine their generated answers through self-correction, enabling continuous performance improvement over multiple rounds.
Approach:	They propose a probabilistic theory to model the dynamics of accuracy change and explain performance improvements observed in multi-round self-correction.
Outcome:	The proposed model can predict accuracy curves and improve accuracy over multiple rounds.

Not All Demonstration Examples are Equally Beneficial: Reweighting Demonstration Examples for In-Context Learning (2023.findings-emnlp)

Copied to clipboard

Zhe Yang, Damai Dai, Peiyi Wang, Zhifang Sui

Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding (2024.findings-acl)

Challenge:	Large Language Models (LLMs) have recently gained the In-Context Learning ability . however, the quality of demonstration examples is usually uneven .
Approach:	They propose to determine optimal weights for demonstration examples and apply them during ICL.
Outcome:	The proposed approach outperforms conventional ICL on 8 classification tasks.

Copied to clipboard

Heming Xia, Zhe Yang, Qingxiu Dong, Peiyi Wang, Yongqi Li, Tao Ge, Tianyu Liu, Wenjie Li, Zhifang Sui

TIGEr: Text-to-Image Grounding for Image Caption Evaluation (D19-1)

Challenge:	Large Language Models (LLMs) have a high inference latency stemming from autoregressive decoding.
Approach:	They propose a novel decoding paradigm that drafts multiple tokens and verifies them in parallel . they aim to provide a catalyst for further research on Speculative Decoding .
Outcome:	The proposed method drafts multiple tokens and verifies them in parallel . it can be used to accelerate inference in large language models.

Copied to clipboard

Ming Jiang, Qiuyuan Huang, Lei Zhang, Xin Wang, Pengchuan Zhang, Zhe Gan, Jana Diesner, Jianfeng Gao

HeteroRAG: A Heterogeneous Retrieval-Augmented Generation Framework for Medical Vision Language Tasks (2026.findings-acl)

Challenge:	Existing metrics based on text-level comparisons fail to assess the quality of captions produced by machines.
Approach:	They propose to use a machine-learned text-image grounding model to measure the accuracy of machine-generated captions and their correlation with human judgments.
Outcome:	The proposed metric has higher consistency with human judgments and is more accurate than existing metrics.

Copied to clipboard

Zhe Chen, Yusheng Liao, Zhiyuan Zhu, Haolin Li, Hongcheng Liu, Yanfeng Wang, Yu Wang

REACT: Representation Extraction And Controllable Tuning to Overcome Overfitting in LLM Knowledge Editing (2025.emnlp-main)

Challenge:	Medical large vision-language models suffer from factual inaccuracies and unreliable outputs.
Approach:	They propose a framework that enhances Med-LVLMs through heterogeneous knowledge sources.
Outcome:	The proposed framework improves Med-LVLMs through heterogeneous knowledge sources.

Copied to clipboard

Haitian Zhong, Yuhuan Liu, Ziyang Xu, Guofan Liu, Qiang Liu, Shu Wu, Zhe Zhao, Liang Wang, Tieniu Tan

SpiderFlow: Efficient Topology-Aware Scheduling for LLM Training Across Decentralized GPU Clusters (2026.acl-long)

Challenge:	Large language model editing methods suffer from overfitting, where factual updates can propagate beyond their intended scope, overemphasizing the edited target even when it’s contextually inappropriate.
Approach:	They propose a framework for precise and controllable knowledge editing that utilizes two-phase representations and a linear transformation to compute a directional "belief shift" vector.
Outcome:	The proposed framework significantly reduces overfitting across nearly all evaluation metrics and on COUNTERFACT and MQuAKE.

Copied to clipboard

Zihan Chang, Shuibing He, Bo Zhou, Sheng Xiao, Siling Yang, Rui Wang, Zhe Pan

An Entity-Driven Framework for Abstractive Summarization (D19-1)

Challenge:	Existing approaches to training large language models lack topologyaware task scheduling mechanisms and model parallelization strategies.
Approach:	They propose a topology-aware scheduling system specifically designed for decentralized GPU clusters . they propose heuristic methods at the inter-cluster level with ILP-based optimization within clusters.
Outcome:	The proposed system reduces job completion time by 1.2-1.3 and improves throughput by 1.12-1.25 . it also reduces scheduling overhead by 20-90 on average compared to state-of-the-art scheduling systems.

Copied to clipboard

Eva Sharma, Luyang Huang, Zhe Hu, Lu Wang

Graph of Attacks with Pruning: Optimizing Stealthy Jailbreak Prompt Generation for Enhanced LLM Content Moderation (2025.emnlp-industry)

Challenge:	Popular neural summarization models produce incoherent and unfaithful summaries . however, their outputs are often incohérent and incoerent .
Approach:	They propose a system for ENtity-drivEn Coherent Abstractive summarization framework that leverages entity information to generate informative and coherent abstracts.
Outcome:	The proposed framework outperforms existing state-of-the-art models on New York Times and CNN/Daily Mail datasets.

Copied to clipboard

Daniel Schwartz, Dmitriy Bespalov, Zhe Wang, Ninad Kulkarni, Yanjun Qi

Exploring Dual Encoder Architectures for Question Answering (2022.emnlp-main)

Challenge:	Existing jailbreaking methods create adversarial prompts to bypass LLM safeguards.
Approach:	They propose a framework for generating stealthy jailbreak prompts that enables knowledge sharing across attack paths.
Outcome:	The proposed framework outperforms state-of-the-art methods for attacking both open and closed LLMs with attack success rates of >96%.

Copied to clipboard

Zhe Dong, Jianmo Ni, Dan Bikel, Enrique Alfonseca, Yuan Wang, Chen Qu, Imed Zitouni

CoAug: Combining Augmentation of Labels and Labelling Rules (2023.findings-acl)

Challenge:	Dual encoders have been used for question-answering and information retrieval tasks with good results.
Approach:	They propose to use two different versions of dual encoders for QA retrieval tasks . they propose to share parameters in projection layers between two encoder towers .
Outcome:	The proposed architectures outperform SDE and ADE on QA retrieval tasks.

Copied to clipboard

Rakesh R. Menon, Bingqing Wang, Jun Araki, Zhengyu Zhou, Zhe Feng, Liu Ren

Edge-free but Structure-aware: Prototype-Guided Knowledge Distillation from GNNs to MLPs (2025.coling-main)

Challenge:	Named Entity Recognition (NER) tasks require large labeled datasets to perform well.
Approach:	They propose a co-augmentation framework that bootstraps predictions from each model to improve few-shot models and rule-augmentation models by bootstrapping them.
Outcome:	The proposed model outperforms strong weak-supervision-based models by 6.5 F1 points . the proposed model can learn from limited labeled data and perform better on small datasets .

Copied to clipboard

Taiqiang Wu, Zhe Zhao, Jiahao Wang, Xingyu Bai, Lei Wang, Ngai Wong, Yujiu Yang

Green & Sustainable NLP

Challenge:	Existing methods to train low-latency multilayer perceptrons (MLPs) on graph tasks are based on graph nodes and lack graph structural information.
Approach:	They propose to distill graph structural information from Graph Neural Networks (GNNs) to low-latency multilayer perceptrons (MLPs) on graph tasks.
Outcome:	The proposed method does not require graph edges (edge-free setting) yet learns structure-aware MLPs.

FastBERT: a Self-distilling BERT with Adaptive Inference Time (2020.acl-main)

Copied to clipboard

Weijie Liu, Peng Zhou, Zhiruo Wang, Zhe Zhao, Haotang Deng, Qi Ju

Embodied-Reasoner: Synergizing Visual Search, Reasoning, and Action for Embodied Interactive Tasks (2026.acl-long)

Challenge:	Pre-trained language models like BERT have proven to be highly performant, but are often computationally expensive in many practical scenarios.
Approach:	They propose a speed-tunable FastBERT with adaptive inference time that can be flexibly adjusted under varying demands.
Outcome:	The proposed model achieves promising results in English and Chinese datasets.

Copied to clipboard

Wenqi Zhang, Mengna Wang, Gangao Liu, Huixin Xu, Yiwei Jiang, Yongliang Shen, Guiyang Hou, Zhe Zheng, Hang Zhang, Xin Li, Jiajun Liu, Weiming Lu, Peng Li, Yueting Zhuang

Online Iterative Self-Alignment for Radiology Report Generation (2025.acl-long)

Challenge:	Recent advances in reasoning models have demonstrated remarkable capabilities on mathematical and coding tasks, but their effectiveness in embodied domains remains largely unexplored.
Approach:	They propose a reasoning model for interactive embodied tasks that synthesizes 9.3k coherent Observation-Thought-Action trajectories containing 64k ego-centric images and 90k diverse reasoning processes.
Outcome:	The proposed model outperforms existing visual reasoning models by +9%, 24%, and +13% on long-horizon tasks.

Copied to clipboard

Ting Xiao, Lei Shi, Yang Zhang, HaoFeng Yang, Zhe Wang, Chenjia Bai

Multi-Fact Correction in Abstractive Text Summarization (2020.emnlp-main)

Challenge:	Existing methods for RRG rely on supervised fine-tuning based on data pairs of radiological images and corresponding radiologist-annotated reports.
Approach:	They propose a method that performs supervised fine-tuning on data pairs of radiological images and corresponding radiologist-annotated reports.
Outcome:	The proposed method surpasses existing methods and achieves state-of-the-art performance across multiple evaluation metrics.

Copied to clipboard

Yue Dong, Shuohang Wang, Zhe Gan, Yu Cheng, Jackie Chi Kit Cheung, Jingjing Liu

Improving Adversarial Text Generation by Modeling the Distant Future (2020.acl-main)

Challenge:	Existing abstractive summarization systems generate incorrect facts with respect to the source text.
Approach:	They propose a suite of two factual correction models that leverages question-answering knowledge to make corrections in system-generated summaries via span selection.
Outcome:	The proposed model improves factuality of news summarization without sacrificing summary quality.

Copied to clipboard

Ruiyi Zhang, Changyou Chen, Zhe Gan, Wenlin Wang, Dinghan Shen, Guoyin Wang, Zheng Wen, Lawrence Carin

Robustness in NLP

Challenge:	Recent work has shown excellent performance on text generation tasks by combining reinforcement learning (RL) and generative models.
Approach:	They propose a model-based imitation-learning approach to improve text generation performance by focusing on a long horizon.
Outcome:	The proposed model improves on a number of text-generation tasks and provides intermediate rewards for generator optimization.

Beyond Agreement: Diagnosing the Rationale Alignment of Automated Essay Scoring Methods based on Linguistically-informed Counterfactuals (2024.findings-emnlp)

Copied to clipboard

Yupei Wang, Renfen Hu, Zhe Zhao

Confusion is the Final Barrier: Rethinking Jailbreak Evaluation and Investigating the Real Misuse Threat of LLMs (2025.findings-emnlp)

Challenge:	Existing Automated Essay Scoring (AES) methods focus on sentence-level features, whereas Large Language Models (LLMs) are sensitive to conventions & accuracy, language complexity, and organization.
Approach:	They propose to use large language models to aid in decision-making . they propose to analyze the reasoning of neural models by analyzing sentence-level features.
Outcome:	The proposed method improves understanding of neural approaches to Automated Essay Scoring (AES) and can also apply to other domains seeking transparency in model-driven decisions.

Copied to clipboard

Yu Yan, Sheng Sun, Zhe Wang, Yijun Lin, Zenghao Duan, Zhifei Zheng, Min Liu, Zhiyi Yin, Jianping Zhang